This work presents a novel platform for autonomous vehicle technologies research for the insurance sector. The platform has been collaboratively developed by the insurance company MAPFRE-CESVIMAP, Universidad Carlos III de Madrid and INSIA of the Universidad Politécnica de Madrid. The high-level architecture and several autonomous vehicle technologies developed using the framework of this collaboration are introduced and described in this work. Computer vision technologies for environment perception, V2X communication capabilities, enhanced localization, human–machine interaction and self awareness are among the technologies which have been developed and tested. Some use cases that validate the technologies presented in the platform are also presented; these use cases include public demonstrations, tests of the technologies and international competitions for self-driving technologies.
Self-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially increasing. One of the main issues in the cooperative driving world is the Multirobot Task Allocation (MRTA) problem. This paper addresses the MRTA problem, specifically for the problem of vehicles and requests allocation. The objective is to introduce a hybrid optimization-based approach to solve the problem of multiple intelligent vehicles requests allocation as an instance of MRTA problem, to find not only a feasible solution, but also an optimized one as per the objective function. Several test scenarios were implemented in order to evaluate the efficiency of the proposed approach. These scenarios are based on well-known benchmarks; thus a comparative study is conducted between the obtained results and the suboptimal results. The analysis of the experimental results shows that the proposed approach was successful in handling various scenarios, especially with the increasing number of vehicles and requests, which displays the proposed approach efficiency and performance.
Este documento presenta la plataforma inteligente de investigación para entornos urbanos llamada iCab (Intelligent Campus Automobile). El principal objetivo es describir los pasos iniciales para alcanzar un vehículo autónomo funcional. La plataforma es un carrito de golf modelo E-Z-GO el cual ha sido modificado para operar de forma autónoma. El núcleo de la arquitectura está basado en ROS (Robot Operating System), y es capaz de recibir información de múltiples sensores con huella temporal utilizando un ordenador embebido. El sistema propuesto muestra las ventajas de la arquitectura basada en ROS para el manejo de datos entre procesos, así como la capacidad de gestionar, procesar y entender la gran cantidad de información recibida por las cámaras o el láser. La información de los sensores se ha integrado en la arquitectura para desarrollar aplicaciones innovadoras capaces de cubrir los requerimientos de las decisiones que tienen que tomar los vehículos autónomos. El ciclo de vida de los proyectos de estas características suele ser corto debido a la evolución de los sistemas de percepción del entorno y la capacidad de procesamiento; por tanto, la utilización de una arquitectura modular, portable e independiente de los sensores es indispensable.
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